Late in December 2019, the World Health Organisation (WHO) China Country Office obtained information about a severe pneumonia of an unknown cause, detected in the city of Wuhan in Hubei province, China. This later turned out to be the novel coronavirus disease (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) of the corona virus family. The disease causes respiratory illness characterised by primary symptoms like cough, fever, and in more acute cases, difficulty in breathing. WHO later declared covid-19 as a Pandemic because of its fast rate of spread across the Globe with over 5.69 Million confirmed cases and over 355,000 deaths as of May 28, 2020. The African continent started confirming its first cases in late January and mid-February of 2020 in some countries. The disease has since spread across all the 54 Africa countries with over 124,000 confirmed cases and over 3,600 deaths as of May 28, 2020.
The covid_19_africa.csv dataset contains daily level information about the Covid-19 cases in Africa. It is a time series data and hence the number of cases on any given day is cumulative.I extracted the data from the covid_19_data.csv which was made available on kaggle. The R script that I used to prepare this dataset is also available on my Github repository. The original datasets can be found on John Hopkins University Github repository for covid_19.
Visualizations of Africa’s COVID-19 cases.
# loading the required packages
suppressMessages(library(tidyverse))## Warning: package 'ggplot2' was built under R version 3.6.3suppressMessages(library(lubridate))
suppressMessages(library(DT))## Warning: package 'DT' was built under R version 3.6.3suppressMessages(library(highcharter))## Warning: package 'highcharter' was built under R version 3.6.3suppressMessages(library(htmltools))
# importing the dataset
covid_africa <- read_csv("covid_19_africa.csv")
# converting the classes of Confirmed, Deaths, and Recovered columns to class integer
covid_africa <- covid_africa %>%
mutate(Region = as_factor(Region),
Confirmed = as.integer(Confirmed),
Deaths = as.integer(Deaths),
Recovered = as.integer(Recovered),
Active = as.integer(Active))knitr::opts_chunk$set(message = FALSE, warning = FALSE)Note that the lastest number of cases are those reported by end of the previous day.
datatable(covid_africa)length(unique(covid_africa$Country))## [1] 54All the 54 African countries recognized by the UN and WHO have confirmed cases of COVID-19.
Note: all plots with legends can be activated or deactivated by clicking on the legend.
# obtaining the cumulative cases by observation date
cases <- covid_africa %>%
group_by(ObservationDate) %>%
summarise(Confirmed = sum(Confirmed), Deaths = sum(Deaths),
Recovered = sum(Recovered), Active = sum(Active))
# specifying the plot colors
colors <- c("Active" = "cyan", "Confirmed" = "orange", "Deaths" = "red", "Recovered" = "lightseagreen")
# plotting
cases %>%
gather(Confirmed, Deaths, Recovered, Active, key = "cases", value = "number") %>%
hchart("line", hcaes(x = ObservationDate, y = number, group = cases),
color = colors) %>%
hc_title(text = "Cumulative number of COVID-19 cases in Africa") %>%
hc_subtitle(text = today()) %>%
hc_xAxis(title = list(text = "Date")) %>%
hc_yAxis(title = list(text = "Number of cases")) %>%
hc_chart(borderColor = "grey",
borderWidth = 1) %>%
hc_tooltip(crosshairs = TRUE, sort = TRUE, table = TRUE) %>%
hc_add_theme(hc_theme_google())# computing the daily new cases and deaths
new_cases <- cases %>%
mutate(new_confirmed = Confirmed - lag(Confirmed, default = first(Confirmed)),
new_deaths = Deaths - lag(Deaths, default = first(Deaths))) %>%
select(ObservationDate, new_confirmed, new_deaths)
# specifying the plot colors
new_colors <- c("new_confirmed" = "orange", "new_deaths" = "red")
# plotting
new_cases %>%
gather(new_confirmed, new_deaths,
key = "cases", value = "number") %>%
hchart("column", hcaes(x = ObservationDate, y = number, group = cases),
color = new_colors) %>%
hc_chart(borderColor = "grey",
borderWidth = 1) %>%
hc_xAxis(title = list(text = "Date")) %>%
hc_yAxis(title = list(text = "Number of cases")) %>%
hc_add_theme(hc_theme_google()) %>%
hc_title(text = "Daily new cases and deaths from COVID-19 in Africa") %>%
hc_tooltip(crosshairs = TRUE, sort = TRUE, table = TRUE) %>%
hc_subtitle(text = today())The case fatality rate and recovery rate are dependant on accurate reporting of COVID-19 cases. The limited testing capacities of some African countries implies that these values may be inexact.
# computing the rates
rates <- cases %>%
mutate(case_fatality = (Deaths / Confirmed) * 100,
recovery_rate = (Recovered / Confirmed) * 100,
CFR = round(case_fatality, 2),
RR = round(recovery_rate, 2))
# specifying the colors
rates_color <- c("CFR" = "red", "RR" = "lightgreen")
# plotting
rates %>%
gather(CFR, RR, key = "cases", value = "number") %>%
hchart("line", hcaes(x = ObservationDate, y = number, group = cases),
color = rates_color) %>%
hc_xAxis(title = list(text = "Date")) %>%
hc_yAxis(title = list(text = "Percent(%)")) %>%
hc_tooltip(crosshairs = TRUE, sort = TRUE, table = TRUE) %>%
hc_title(text = "Changes in the Case Fatality Rate(CFR) and Recovery Rate(RR) in Africa") %>%
hc_chart(borderColor = "grey",
borderWidth = 1) %>%
hc_add_theme(hc_theme_google())# getting the latest number of cases of COVID-19 in Africa by country
latest <- covid_africa %>%
filter(ObservationDate == today() - 1 )
# downloading a map of Africa
africa_map <- download_map_data("https://code.highcharts.com/mapdata/custom/africa.js")
# looking and the African Countries
properties <- get_data_from_map(africa_map)
# modify some country names in the covid_19_africa data to match those in africa_map
latest_1 <- latest %>%
mutate(Country = replace(Country, Country == "Congo (Kinshasa)", "Democratic Republic of the Congo"),
Country = replace(Country, Country == "Congo (Brazzaville)", "Republic of Congo"),
Country = replace(Country, Country == "Tanzania", "United Republic of Tanzania"))highchart() %>%
hc_add_series_map(africa_map, latest_1, value = "Confirmed",
joinBy = c("name", "Country"), name = "Confirmed") %>%
hc_colorAxis(minColor = "lightyellow", maxColor = "Darkorange") %>%
hc_mapNavigation(enabled = TRUE) %>%
hc_title(text = "Confirmed") %>%
hc_subtitle(text = today()) %>%
hc_chart(borderColor = "grey",
borderWidth = 1)highchart() %>%
hc_add_series_map(africa_map, latest_1, value = "Deaths",
joinBy = c("name", "Country"), name = "Deaths") %>%
hc_colorAxis(minColor = "snow", maxColor = "darkred") %>%
hc_mapNavigation(enabled = TRUE) %>%
hc_title(text = "Deaths") %>%
hc_subtitle(text = today()) %>%
hc_chart(borderColor = "grey",
borderWidth = 1)highchart() %>%
hc_add_series_map(africa_map, latest_1, value = "Recovered",
joinBy = c("name", "Country"), name = "Recovered") %>%
hc_colorAxis(minColor = "lightgreen", maxColor = "darkgreen") %>%
hc_mapNavigation(enabled = TRUE) %>%
hc_title(text = "Recovered") %>%
hc_subtitle(text = today()) %>%
hc_chart(borderColor = "grey",
borderWidth = 1)highchart() %>%
hc_add_series_map(africa_map, latest_1, value = "Active",
joinBy = c("name", "Country"), name = "Active") %>%
hc_colorAxis(minColor = "lightcyan", maxColor = "darkcyan") %>%
hc_mapNavigation(enabled = TRUE) %>%
hc_title(text = "Active") %>%
hc_subtitle(text = today()) %>%
hc_chart(borderColor = "grey",
borderWidth = 1)# computing the regional cases of COVID-19
regional_cases <- covid_africa %>%
group_by(ObservationDate, Region) %>%
summarise(Confirmed = sum(Confirmed), Deaths = sum(Deaths),
Recovered = sum(Recovered), Active = sum(Active))# plotting
regional_cases %>%
hchart("line", hcaes(x = ObservationDate, y = Confirmed, group = Region)) %>%
hc_xAxis(title = list(text = "Date")) %>%
hc_title(text = "Cumulative Confirmed cases") %>%
hc_subtitle(text = today()) %>%
hc_chart(borderColor = "grey",
borderWidth = 1) %>%
hc_tooltip(crosshairs = TRUE, sort = TRUE, table = TRUE) %>%
hc_add_theme(hc_theme_google())regional_cases %>%
hchart("line", hcaes(x = ObservationDate, y = Deaths, group = Region)) %>%
hc_xAxis(title = list(text = "Date")) %>%
hc_title(text = "Cumulative Deaths") %>%
hc_subtitle(text = today()) %>%
hc_chart(borderColor = "grey",
borderWidth = 1) %>%
hc_tooltip(crosshairs = TRUE, sort = TRUE, table = TRUE) %>%
hc_add_theme(hc_theme_google())regional_cases %>%
hchart("line", hcaes(x = ObservationDate, y = Recovered, group = Region)) %>%
hc_xAxis(title = list(text = "Date")) %>%
hc_title(text = "Cumulative Recoveries") %>%
hc_subtitle(text = today()) %>%
hc_chart(borderColor = "grey",
borderWidth = 1) %>%
hc_tooltip(crosshairs = TRUE, sort = TRUE, table = TRUE) %>%
hc_add_theme(hc_theme_google())regional_cases %>%
hchart("line", hcaes(x = ObservationDate, y = Active, group = Region)) %>%
hc_xAxis(title = list(text = "Date")) %>%
hc_title(text = "Cumulative Active cases") %>%
hc_subtitle(text = today()) %>%
hc_chart(borderColor = "grey",
borderWidth = 1) %>%
hc_tooltip(crosshairs = TRUE, sort = TRUE, table = TRUE) %>%
hc_add_theme(hc_theme_google())# computing the regional CFR and RR
regional_rates <- latest %>%
group_by(Region) %>%
summarise(Confirmed = sum(Confirmed), Deaths = sum(Deaths), Recovered = sum(Recovered)) %>%
mutate(case_fatality = (Deaths / Confirmed) * 100,
fatality_rounded = round(case_fatality, 2),
recovery_rate = (Recovered / Confirmed) * 100,
recovery_rounded = round(recovery_rate, 2))
# plotting
regional_fatality <- regional_rates %>%
arrange(case_fatality) %>%
hchart("column", hcaes(Region, fatality_rounded), color = "red", name = "CFR(%)") %>%
hc_yAxis(title = list(text = "Case Fatality Rate (%)")) %>%
hc_title(text = "Case Fatality Rate by African Region") %>%
hc_subtitle(text = today()) %>%
hc_chart(borderColor = "grey",
borderWidth = 1) %>%
hc_add_theme(hc_theme_google())
regional_recovery <- regional_rates %>%
arrange(recovery_rate) %>%
hchart("column", hcaes(Region, recovery_rounded), color = "green", name = "RR(%)") %>%
hc_yAxis(title = list(text = "Recovery Rate (%)")) %>%
hc_title(text = "Recovery Rate by African Region") %>%
hc_subtitle(text = today()) %>%
hc_chart(borderColor = "grey",
borderWidth = 1) %>%
hc_add_theme(hc_theme_google())
# all plots on same page
all_rates <- list(regional_fatality, regional_recovery)
hw_grid(all_rates, rowheight = 400, ncol = 2) %>% browsable()# getting the latest number of confirmed cases in descending order
latest_confirmed <- latest %>%
arrange(desc(Confirmed))# plotting
highchart() %>%
hc_add_series(data = latest_confirmed, type = "bar",
hcaes(x = Country, y = Confirmed),
name = "Confirmed cases", showInLegend = FALSE,
color = "orange") %>%
hc_xAxis(categories = latest_confirmed$Country,
title = list(text = "Country")) %>%
hc_yAxis(title = list(text = "Confirmed cases")) %>%
hc_title(text = "COVID-19 Confirmed cases by African Country") %>%
hc_subtitle(text = today()) %>%
hc_chart(borderColor = "grey",
borderWidth = 1) %>%
hc_size(900, 900)# COVID-19 deaths in descending order
latest_deaths <- latest %>%
arrange(desc(Deaths))
# plotting
highchart() %>%
hc_add_series(data = latest_deaths, type = "bar",
hcaes(x = Country, y = Deaths),
name = "Deaths", showInLegend = FALSE,
color = "red") %>%
hc_xAxis(categories = latest_deaths$Country,
title = list(text = "Country")) %>%
hc_yAxis(title = list(text = "Deaths")) %>%
hc_title(text = "Deaths from COVID-19 by Country") %>%
hc_subtitle(text = today()) %>%
hc_chart(borderColor = "grey",
borderWidth = 1) %>%
hc_size(900, 900)# recovered cases in descending order
latest_recovered <- latest %>%
arrange(desc(Recovered))
# plotting
highchart() %>%
hc_add_series(data = latest_recovered, type = "bar",
hcaes(x = Country, y = Recovered),
name = "Recovered cases", showInLegend = FALSE,
color = "lightseagreen") %>%
hc_xAxis(categories = latest_recovered$Country,
title = list(text = "Country")) %>%
hc_yAxis(title = list(text = "Recovered cases")) %>%
hc_title(text = "COVID-19 recoveries by Country") %>%
hc_subtitle(text = today()) %>%
hc_chart(borderColor = "grey",
borderWidth = 1) %>%
hc_size(900, 900)# active cases in ascending order
latest_active <- latest %>%
arrange(desc(Active))
# plotting
highchart() %>%
hc_add_series(data = latest_active, type = "bar",
hcaes(x = Country, y = Active),
name = "Active cases", showInLegend = FALSE,
color = "cyan") %>%
hc_xAxis(categories = latest_active$Country,
title = list(text = "Country")) %>%
hc_yAxis(title = list(text = "Active cases")) %>%
hc_title(text = "Active cases of COVID-19 in Africa by Country") %>%
hc_subtitle(text = today()) %>%
hc_chart(borderColor = "grey",
borderWidth = 1) %>%
hc_size(900, 900)latest %>%
mutate(case_fatality = (Deaths / Confirmed) * 100,
fatality_rounded = round(case_fatality, 2),
recovery_rate = (Recovered / Confirmed) * 100,
recovery_rounded = round(recovery_rate, 2)) %>%
rename("Case Fatality Rate(%)" = fatality_rounded,
"Recovery Rate(%)" = recovery_rounded) %>%
select(Country, Region, "Case Fatality Rate(%)", "Recovery Rate(%)") %>%
datatable()Compare the changes in the number of COVID-19 cases in your countries of choice
covid_africa %>%
hchart("spline", hcaes(ObservationDate, Confirmed, group = Country), visible = FALSE) %>%
hc_xAxis(title = list(text = "Date")) %>%
hc_title(text = "Confirmed cases") %>%
hc_subtitle(text = today()) %>%
hc_chart(borderColor = "grey",
borderWidth = 1) %>%
hc_tooltip(crosshairs = TRUE, sort = TRUE, table = TRUE) %>%
hc_add_theme(hc_theme_google()) %>%
hc_size(800, 600)Compare changes in the number of COVID-19 deaths in your countries of choice
covid_africa %>%
hchart("spline", hcaes(ObservationDate, Deaths, group = Country), visible = FALSE) %>%
hc_xAxis(title = list(text = "Date")) %>%
hc_title(text = "Deaths") %>%
hc_subtitle(text = today()) %>%
hc_chart(borderColor = "grey",
borderWidth = 1) %>%
hc_tooltip(crosshairs = TRUE, sort = TRUE, table = TRUE) %>%
hc_add_theme(hc_theme_google()) %>%
hc_size(800, 600)Compare changes in the number of COVID-19 recoveries in your countries of choice
covid_africa %>%
hchart("spline", hcaes(ObservationDate, Recovered, group = Country),
visible = FALSE) %>%
hc_xAxis(title = list(text = "Date")) %>%
hc_title(text = "Deaths") %>%
hc_subtitle(text = today()) %>%
hc_chart(borderColor = "grey",
borderWidth = 1) %>%
hc_tooltip(crosshairs = TRUE, sort = TRUE, table = TRUE) %>%
hc_add_theme(hc_theme_google()) %>%
hc_size(800, 600)Compare changes in the number of COVID-19 Active cases in your countries of choice
covid_africa %>%
hchart("spline", hcaes(ObservationDate, Active, group = Country),
visible = FALSE) %>%
hc_xAxis(title = list(text = "Date")) %>%
hc_title(text = "Active") %>%
hc_subtitle(text = today()) %>%
hc_chart(borderColor = "grey",
borderWidth = 1) %>%
hc_tooltip(crosshairs = TRUE, sort = TRUE, table = TRUE) %>%
hc_add_theme(hc_theme_google()) %>%
hc_size(800, 600)